Demand-side management and demand response have been developed as techniques for controlling the appliances of consumers to reduce the power at the peak of demand. For example, when power demand is expected to exceed an acceptable amount, a power company remotely operates the temperature setting of the air conditioner in every home so that the temperature is not set to 28° C. or lower. Further, in an alternative way, power price is increased to restrain customers from using electric appliances. In this way, the power at the peak of demand can be reduced, and the power company is not required to make an excessive investment in the facilities. Further, fossil fuel which must be used to generate power at the peak of demand can be reduced, which leads to effective reduction of CO2.
It is important to previously predict how much energy can be reduced when uniformly changing temperature setting or when increasing the price. When the request for customers is underestimated, power consumption cannot be reduced enough, while when the request for customers is overestimated, customers have complaints since they are forced to excessively reduce power consumption.
Conventionally, the technique of demand response was used to reduce the power consumption as required while minimizing the total complaint cost, by modeling the relationship between the use situations of home appliances particularly air conditioners and the complaint cost of users while modeling the relationship between the power price and the complaint cost.
However, it is difficult to say that the operational situation of the air conditioner can be accurately simulated by this method, which is because this method is used to determine the operation and temperature set value of the air conditioner based on the outdoor air temperature by using a table, or based on the outdoor air temperature and the average temperature set value of the air conditioner in the past. That is, modeling is performed covering a situation where no inhabitant exists in the house and a situation where the inhabitant is doing exercising in the room, and thus a situation where the air conditioner is not turned on when the outdoor air temperature is high and the room temperature is also high is treated as a modeling target. Further, penalties are similarly calculated on a case where the temperature of the air conditioner is set to low when the sensible temperature of the inhabitant is high since he/she is doing exercising in the room, and on a case where the temperature of the air conditioner is set to low when the inhabitant is staying still in the room.